Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

	Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau
	Cui, Yaokui 1; Long, Di 1; Hong, Yang 1; Zeng, Chao 1; Zhou, Jie 1; Han, Zhongying 1; Liu, Ronghua 1; Wan, Wei 1
刊名	Journal of Hydrology
	2016
卷号	543 页码:242-254
关键词	REGIONAL ENVIRONMENTAL ASSESSMENTS ET-AL. 2015 TERRESTRIAL ECOSYSTEMS ATMOSPHERIC NITROGEN CHEMICAL-COMPOSITION NORTHERN CHINA SULFUR PRECIPITATION INCREASE AGROECOSYSTEM
通讯作者	Long, Di (dlong@tsinghua.edu.cn)
英文摘要	Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the Earth's ‘third pole’. Large-scale spatially consistent and temporally continuous soil moisture datasets are of great importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is a relatively new passive microwave product, with the satellite being launched on November 5, 2010. This study validates and reconstructs FY-3B/MWRI soil moisture across the TP. First, the validation is performed using in situ measurements within two in situ soil moisture measurement networks (1° × 1° and 0.25° × 0.25°), and also compared with the Essential Climate Variable (ECV) soil moisture product from multiple active and passive satellite soil moisture products using new merging procedures. Results show that the ascending FY-3B/MWRI product outperforms the descending product. The ascending FY-3B/MWRI product has almost the same correlation as the ECV product with the in situ measurements. The ascending FY-3B/MWRI product has better performance than the ECV product in the frozen season and under the lower NDVI condition. When the NDVI is higher in the unfrozen season, uncertainty in the ascending FY-3B/MWRI product increases with increasing NDVI, but it could still capture the variability in soil moisture. Second, the FY-3B/MWRI soil moisture product is subsequently reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and NDVI, LST, and albedo, but also the relationship between the soil moisture and four-dimensional variations using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R2higher than 0.56, RMSE less than 0.1 cm3 cm−3, and Bias less than 0.07 cm3 cm−3for both frozen and unfrozen seasons, compared with the in situ measurements at the two networks. Third, the reconstruction method is applied to generate surface soil moisture over the TP. Both original and reconstructed FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, and ecosystems over the TP. © 2016 Elsevier B.V.
学科主题	Engineering; Geology; Water Resources
类目[WOS]	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20164302950442
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39495]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 2.100084, China 3. Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman 4.OK 5.73019, United States 6. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 7.100101, China 8. Delft University of Technology, Delft, Netherlands 9. China Institute of Water Resources and Hydropower Research, Beijing 10.100038, China
推荐引用方式 GB/T 7714	Cui, Yaokui,Long, Di,Hong, Yang,et al. Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau[J]. Journal of Hydrology,2016,543:242-254.
APA	Cui, Yaokui.,Long, Di.,Hong, Yang.,Zeng, Chao.,Zhou, Jie.,...&Wan, Wei.(2016).Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau.Journal of Hydrology,543,242-254.
MLA	Cui, Yaokui,et al."Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau".Journal of Hydrology 543(2016):242-254.